Analysis of secondary data: Considerations revisited
Bibliographic record
Abstract
In a recent publication, we discussed the benefits and cautions of using secondary data analyses in research on lifestyle and health behavior [1]. We provided some guidelines about the use of secondary data in terms of the contributions that can be made and at the same time considerations necessary in using data that are collected by someone else. The use of secondary data to explore social and health issues results in being able to provide information about important issues in a timely fashion. Secondary data can answer two types of questions: descriptive and analytical [2]. Hence, the information can be used to describe events or trends or it can be used to examine relationships among variables cross-sectionally or longitudinally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.770 | 0.848 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".